Prediction of Throughput of EXT WLANs through Machine Learning

Rajasekar Mohan, Kartikey Mishra, Abhinav Reddy Bendrapu, Burra Venkata Vasishta, Boru Andreswar Reddy
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Abstract

WLANs are expected to be one of the foundations of next-generation wireless communication systems. They're well-known for their unique capacity to deliver high data speeds in key places (hotspots).WLANs, with IEEE 802.11 as the industry's most widely accepted standard, are a cost-effective alternative for wireless internet connection that can cover most of today's communication needs in residential and business contexts. However, the scarcity of frequency spectrum in the ISM radio bands, growing throughput demands from new bandwidth-demanding applications, and the heterogeneity of current wireless network architecture all contribute to tremendous complexity [1]. In dense WLAN deployments, such concerns become more important, resulting in several partially overlapping scenarios and coexistence issues.
基于机器学习的EXT无线局域网吞吐量预测
无线局域网有望成为下一代无线通信系统的基础之一。它们以其在关键位置(热点)提供高速数据的独特能力而闻名。无线局域网,以IEEE 802.11作为业界最广泛接受的标准,是一种具有成本效益的无线互联网连接替代方案,可以覆盖当今住宅和商业环境中的大多数通信需求。然而,ISM无线电频段频谱的稀缺性、新的带宽要求苛刻的应用对吞吐量的不断增长的需求以及当前无线网络架构的异构性都导致了极大的复杂性[1]。在密集的WLAN部署中,这些问题变得更加重要,从而导致几个部分重叠的场景和共存问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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